{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient descent and stochastic gradient descent with `Gluon`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:09.126546Z",
     "start_time": "2017-10-22T11:33:08.473895Z"
    }
   },
   "outputs": [],
   "source": [
    "import mxnet as mx\n",
    "from mxnet import autograd\n",
    "from mxnet import gluon\n",
    "from mxnet import ndarray as nd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "mx.random.seed(1)\n",
    "random.seed(1)\n",
    "\n",
    "# Generate data.\n",
    "num_inputs = 2\n",
    "num_examples = 1000\n",
    "true_w = [2, -3.4]\n",
    "true_b = 4.2\n",
    "X = nd.random_normal(scale=1, shape=(num_examples, num_inputs))\n",
    "y = true_w[0] * X[:, 0] + true_w[1] * X[:, 1] + true_b\n",
    "y += .01 * nd.random_normal(scale=1, shape=y.shape)\n",
    "dataset = gluon.data.ArrayDataset(X, y)\n",
    "\n",
    "net = gluon.nn.Sequential()\n",
    "net.add(gluon.nn.Dense(1))\n",
    "square_loss = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:09.372138Z",
     "start_time": "2017-10-22T11:33:09.130188Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def train(batch_size, lr, epochs, period):\n",
    "    assert period >= batch_size and period % batch_size == 0\n",
    "    net.collect_params().initialize(mx.init.Normal(sigma=1), force_reinit=True)\n",
    "    # SGD.\n",
    "    trainer = gluon.Trainer(net.collect_params(), 'sgd',\n",
    "                            {'learning_rate': lr})\n",
    "    data_iter = gluon.data.DataLoader(dataset, batch_size, shuffle=True)\n",
    "    total_loss = [np.mean(square_loss(net(X), y).asnumpy())]\n",
    "    for epoch in range(1, epochs + 1):\n",
    "        # Decay learning rate.\n",
    "        if epoch > 2:\n",
    "            trainer.set_learning_rate(trainer.learning_rate * 0.1)\n",
    "        for batch_i, (data, label) in enumerate(data_iter):\n",
    "            with autograd.record():\n",
    "                output = net(data)\n",
    "                loss = square_loss(output, label)\n",
    "            loss.backward()\n",
    "            trainer.step(batch_size)\n",
    "            if batch_i * batch_size % period == 0:\n",
    "                total_loss.append(np.mean(square_loss(net(X), y).asnumpy()))\n",
    "        print(\"Batch size %d, Learning rate %f, Epoch %d, loss %.4e\" % \n",
    "              (batch_size, trainer.learning_rate, epoch, total_loss[-1]))\n",
    "\n",
    "    print('w:', np.reshape(net[0].weight.data().asnumpy(), (1, -1)), \n",
    "          'b:', net[0].bias.data().asnumpy()[0], '\\n')\n",
    "    x_axis = np.linspace(0, epochs, len(total_loss), endpoint=True)\n",
    "    plt.semilogy(x_axis, total_loss)\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('loss')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:12.709445Z",
     "start_time": "2017-10-22T11:33:09.373924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size 1, Learning rate 0.200000, Epoch 1, loss 5.5937e-05\n",
      "Batch size 1, Learning rate 0.200000, Epoch 2, loss 8.0472e-05\n",
      "Batch size 1, Learning rate 0.020000, Epoch 3, loss 4.9757e-05\n",
      "w: [[ 1.99949276 -3.39981604]] b: 4.19997 \n",
      "\n"
     ]
    },
    {
     "data": {
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EylxJzZ0cb/Jd3iPW2oVy1qnsrpsVG0KTwr9NYxvD3wAAICDpEiobJWV3cjzHd3mi3Svp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\nh3nG8LfvaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a8f8780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(batch_size=1, lr=0.2, epochs=3, period=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:13.431129Z",
     "start_time": "2017-10-22T11:33:12.712008Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size 1000, Learning rate 0.999000, Epoch 1, loss 1.1561e-01\n",
      "Batch size 1000, Learning rate 0.999000, Epoch 2, loss 8.4421e-04\n",
      "Batch size 1000, Learning rate 0.099900, Epoch 3, loss 6.9547e-04\n",
      "w: [[ 2.00893021 -3.36536145]] b: 4.19384 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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o36OKWZMPZUi/3NUt0+5/hv+898mMp5IkZSGf5ecbUkoLU0rPNln/Vkrp903X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\nSZIkDWZSKUmSpMFMKiVJkjTYfwFVoZTOUjaaPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac62be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(batch_size=1000, lr=0.999, epochs=3, period=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:14.988893Z",
     "start_time": "2017-10-22T11:33:13.433880Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size 10, Learning rate 0.200000, Epoch 1, loss 4.9184e-05\n",
      "Batch size 10, Learning rate 0.200000, Epoch 2, loss 4.9389e-05\n",
      "Batch size 10, Learning rate 0.020000, Epoch 3, loss 4.8990e-05\n",
      "w: [[ 1.99998689 -3.39983392]] b: 4.20028 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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RqDzuXJWSJNU1Q6WWbEN+oA7YUilJUr0zVGrJWpsaWdvZAtinUpKkemeoVElm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\nQ6UkSZJaM1RKkiSptX8BngR5HYCSa7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac33f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(batch_size=10, lr=0.2, epochs=3, period=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:16.421230Z",
     "start_time": "2017-10-22T11:33:14.991704Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size 10, Learning rate 5.000000, Epoch 1, loss nan\n",
      "Batch size 10, Learning rate 5.000000, Epoch 2, loss nan\n",
      "Batch size 10, Learning rate 0.500000, Epoch 3, loss nan\n",
      "w: [[ nan  nan]] b: nan \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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MioiEMGst81OzeXz+GjJyizz1E3u05p6zetM7vpmD3YlIIFCYFBEJUcsz8njk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\n/w/c4IIGeS7EjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a584208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(batch_size=10, lr=5, epochs=3, period=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-22T11:33:17.815904Z",
     "start_time": "2017-10-22T11:33:16.423952Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size 10, Learning rate 0.002000, Epoch 1, loss 9.1293e+00\n",
      "Batch size 10, Learning rate 0.002000, Epoch 2, loss 6.1059e+00\n",
      "Batch size 10, Learning rate 0.000200, Epoch 3, loss 5.8656e+00\n",
      "w: [[ 0.9720636  -1.67973948]] b: 1.42253 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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+L7t1awnAW58u48y/jXXilCSliGQu1H8t0A04HfifusdpQLcoiq5L1n4kKRm6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105509240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(batch_size=10, lr=0.002, epochs=3, period=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next\n",
    "[Momentum from scratch](../chapter06_optimization/momentum-scratch.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For whinges or inquiries, [open an issue on  GitHub.](https://github.com/zackchase/mxnet-the-straight-dope)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
